Color Features Extraction and Classification of Digital Images of Erythrocytes Infected by Plasmodium berghei

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dc.contributor.authorLorenzo Ginori, Juan Valentín
dc.contributor.authorMeneses Marcel, Alfredo
dc.contributor.authorMollineda Diogo, Niurka
dc.contributor.authorOrozco Morales, Rubén
dc.contributor.authorSifontes Rodríguez, Sergio
dc.contributor.authorChinea Valdés, Lyanett
dc.contributor.authorIzquierdo Torres, Yanela
dc.contributor.departmentUniversidad Central "Marta Abreu" de Las Villas. Dpto de Automáticaen_US
dc.contributor.otherUniversidad Central "Marta Abreu" de Las Villas. Centro de Bioactivos Químicos,en_US
dc.date.accessioned2022-02-17T16:06:10Z
dc.date.available2022-02-17T16:06:10Z
dc.date.issued2019-11
dc.description.abstractThe development of antimalarial drugs requires performing laboratory experiments that include the analysis of blood smears infected with Plasmodium berghei. Analyzing visually the resulting microscopy images is usually a slow and tedious task prone to errors due to fatigue and subjectivity of the analysts. These facts motivated the creation of digital image processing systems to automate the aforementioned analysis. We present in this work a computer vision solution which processes microscopy images of blood smears. This system performs tasks like illumination correction, color compensation, image segmentation including separation of clumped objects and the extraction and selection of color features. Then a set of classifiers was tested to find the best one in terms of classification results. Here a new feature named pixels fraction was introduced and a number of other color features were extracted, from which a subset was selected for the classification of the cells into either normal or infected. The classifiers tested for this application were: support vector machines (SVM), K-nearest neighbors (KNN), J48, Random Forest (RF), Naïve Bayes and linear discriminant analysis (LDA). All of them were evaluated in terms of their performance expressed as correct classification rate, sensitivity, specificity, F-measure and area under Receiver Operating Characteristic (ROC) curve (AUC). The usefulness of the pixels fraction as a new and effective feature was demonstrated by the experimental results. In regard of classifiers, J48 and Random Forest showed the best results.en_US
dc.identifier.doi10.1007/978-3-030-13469-3_83en_US
dc.identifier.urihttps://dspace.uclv.edu.cu/handle/123456789/13436
dc.language.isoen_USen_US
dc.relation.journalLecture Notes in Computer Scienceen_US
dc.source.endpage722en_US
dc.source.initialpage715en_US
dc.source.volume11401en_US
dc.subjectMalariaen_US
dc.subjectImage processingen_US
dc.subjectComputer visionen_US
dc.subjectFeature extractionen_US
dc.titleColor Features Extraction and Classification of Digital Images of Erythrocytes Infected by Plasmodium bergheien_US
dc.typeArticleen_US
dc.type.article1en_US

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Artículos - Centro de Estudios de Mecánica Computacional Aplicada a Ingeniería